Results 21 to 30 of about 1,314,055 (250)
A storage-efficient ensemble classification using filter sharing on binarized convolutional neural networks [PDF]
PeerJ Computer Science, 2022 This paper proposes a storage-efficient ensemble classification to overcome the low inference accuracy of binary neural networks (BNNs). When external power is enough in a dynamic powered system, classification results can be enhanced by aggregating ...
HyunJin Kim +2 more
doaj +2 more sources
CodNN – Robust Neural Networks From Coded Classification [PDF]
, 2020 Deep Neural Networks (DNNs) are a revolutionary force in the ongoing information revolution, and yet their intrinsic properties remain a mystery. In particular, it is widely known that DNNs are highly sensitive to noise, whether adversarial or random ...
Bruck, Jehoshua +4 more
core +5 more sources
BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks [PDF]
International Conference on Computer Aided Verification, 2021 Verifying and explaining the behavior of neural networks is becoming increasingly important, especially when they are deployed in safety-critical applications.
Yedi Zhang +4 more
semanticscholar +1 more source
Elastic-Link for Binarized Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Recent work has shown that Binarized Neural Networks (BNNs) are able to greatly reduce computational costs and memory footprints, facilitating model deployment on resource-constrained devices. However, in comparison to their full-precision counterparts, BNNs suffer from severe accuracy degradation.
Hu, Jie +5 more
openaire +2 more sources
Reliable Binarized Neural Networks on Unreliable Beyond Von-Neumann Architecture
IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2022 Specialized hardware accelerators beyond von-Neumann, that offer processing capability in where the data resides without moving it, become inevitable in data-centric computing.
Mikail Yayla +5 more
semanticscholar +1 more source
Hardware-Efficient Stochastic Binary CNN Architectures for Near-Sensor Computing
Frontiers in Neuroscience, 2022 With recent advances in the field of artificial intelligence (AI) such as binarized neural networks (BNNs), a wide variety of vision applications with energy-optimized implementations have become possible at the edge.
Vivek Parmar +3 more
doaj +1 more source
FeFET-Based Binarized Neural Networks Under Temperature-Dependent Bit Errors
IEEE transactions on computers, 2022 Ferroelectric FET (FeFET) is a highly promising emerging non-volatile memory (NVM) technology, especially for binarized neural network (BNN) inference on the low-power edge.
Mikail Yayla +7 more
semanticscholar +1 more source
SFAO: Sign-Flipping-Aware Optimization for Early-Stopping of Binarized Neural Networks
IEEE Access, 2023 One of the vital challenges for the binary neural networks (BNNs) is improving their inference performance by expanding their data representation capabilities for figuring out delicate patterns and nuances in the data.
Ju Yeon Kang +3 more
doaj +1 more source
Precise Quantitative Analysis of Binarized Neural Networks: A BDD-based Approach
ACM Transactions on Software Engineering and Methodology, 2022 As a new programming paradigm, neural-network-based machine learning has expanded its application to many real-world problems. Due to the black-box nature of neural networks, verifying and explaining their behavior are becoming increasingly important ...
Yedi Zhang +4 more
semanticscholar +1 more source
Memory-Efficient Training of Binarized Neural Networks on the Edge
Design Automation Conference, 2022 A visionary computing paradigm is to train resource efficient neural networks on the edge using dedicated low-power accelerators instead of cloud infrastructures, eliminating communication overheads and privacy concerns.
Mikail Yayla, Jian-Jia Chen
semanticscholar +1 more source